Most techniques and network architectures employed in building wireless infrastructure and deploying wireless services today make it difficult to unlock the full potential of state of the art wireless technologies in both rural and urban areas.
Today's wireless networks suffer from capacity shortages due to the large proliferation of data-hungry devices like smart phones, tablets, and notebooks. The number of devices accessing the data-network is expected to increase at an exponential rate in the years to come. Even when the number of devices on the network begins to saturate, the applications driving the data demand will continue to grow. These applications include on-line gaming, video conferencing, high definition video, and file sharing vary in their latency and bandwidth requirements.
While most urban areas face a capacity crunch, rural areas remain largely underserved for a wide variety of reasons including long distance, poor infrastructure, and scarcity of skilled labor. The key aspect for these networks is to solve the connectivity problem while meeting the cost and power requirements.
Lots of the research in wireless technology is focusing on multi-antenna beamforming or space-time-adaptive-processing (STAP) (In this disclosure, the terms STAP, SFAP (space-frequency-adaptive processing), and STFAP (space-time-frequency-adaptive-processing) are used interchangeably, and all refer to the processing of degrees of freedom (DOFs) and channel equalization in all three dimensions (time, frequency, and space). See Section 2.3 for more details) and under-utilized frequency bands like mm-Wave for solving both urban and rural capacity and connectivity problems. STAP allows real-time dynamic beam-steering and pattern shaping. For rural areas, this means that long-distance links with highly focused beams can be established in minutes with little or no manual alignment required. For urban areas, this means that links can make better use of multi-path scattering and reflections in order to improve link distance, reliability, and coverage. It also improves the co-existence among links operating simultaneously in the same frequency channel, and thus increases the spectral efficiency of wireless networks. mm-Wave frequency bands have large chunks of open spectrum (on the order of several GHz) that remain extremely underutilized. These relatively open spectrum bands present a great opportunity to alleviate the spectrum congestion in lower bands.
There are several challenges that make it difficult to effectively leverage those techniques. The first challenge is the hardware limitation. STAP algorithms and mm-Wave bands require processing large chunks of data in real-time. The amount of data that needs to be processed scales linearly with the product of the signal bandwidth and the number of antennas (channels) that are connected to the digital baseband. The processing requirements scale approximately linearly with the product of the signal bandwidth and the somewhere between the square and the cube of the number of antennas depending on the STAP algorithm being used. The increase in capacity and link budget that comes with STAP is proportional to the number of antennas. At mm-Wave frequencies, the available bandwidth can be 10-100 times larger than what is currently available for systems operating in cellular and microwave bands. Therefore, the processing requirement becomes a lot larger when STAP is used in conjunction with mm-Wave frequency bands. However, the recent advances in integrated circuit (IC) technology, especially CMOS, seems to have addressed lots of these limitations. With the latest CMOS technology, it is possible to build high density digital circuits that can meet the processing requirements at low cost and power consumption.
Furthermore, the latest silicon technologies (e.g. CMOS, SiGe, BiCMOS) have enabled ICs to operate at high frequencies (e.g. mm-Wave) that were only attainable with expensive processes (e.g. GaAs), allowing tighter integration between RF/analog and digital components, which leads to higher cost reduction.
The second important challenge comes from the wireless channel, which is a function of how networks are currently being deployed. Most wireless data networks are either terrestrial or satellite based. The most prevalent example of outdoor terrestrial networks are cellular fixed and mobile access and backhaul networks (examples shown in FIGS. 22, 23, 25, and 26). In these systems, one or both end of a link is mounted on a tower or a pole or a building. In traditional macro-cellular networks (with sparse base-station deployments), base-stations are usually mounted on towers and rooftops of tall buildings to achieve wide coverage areas. However, with capacity becoming more of an issue recently, cellular carriers have been increasingly shifting their focus towards small cell deployments. Small cells are usually mounted on light poles, street lights, building walls, and rooftops of small buildings. In mobile networks, the other end of the link is usually indoors and/or at street level. In fixed wireless networks, the other end of the link is usually mounted on top of residential and office building. In P2P microwave networks, both ends of the link are usually mounted on tall towers or buildings in order to achieve LOS. In P2P and P2MP NLOS small cell backhaul networks, one end of the link will be on a tall tower or a building (at the macro-cell site) while the end will be on a light pole or a building wall (at the small cell site). One of the nodes (but usually not both) can also be mobile.
These types of terrestrial deployments present the following challenges for a wireless system:
Path Loss
The baseline path loss model for wireless links is governed by the Friis (free space) Equation:
                              L          ⁡                      (                          r              ,              λ                        )                          =                                                            P                r                            ⁡                              (                                  r                  ,                  λ                                )                                                    P              t                                =                                                    λ                2                            ⁢                                                G                  Tx                                ⁡                                  (                  λ                  )                                            ⁢                                                G                  Rx                                ⁡                                  (                  λ                  )                                                                                    (                                  4                  ⁢                  π                  ⁢                                                                          ⁢                  r                                )                            2                                                          (        1        )            
The left side of the equation is the path loss (the ratio of the received power Pr to the transmitted power Pt) as a function of distance r and wavelength λ. GTx and GRx are the Tx and Rx antenna gains respectively at the wavelength (i.e. frequency) of interest. (The antenna gains are unitless and are assumed to be computed at the direction pointing towards the other end of the link.) Equation 1 is also known as the square law equation since the received power is inversely proportional to the square of the distance. Equation 1 also assumes LOS only propagation in free space. To account for other mediums of propagation, the right hand side of Equation 1 is multiplied by an exponentially decaying term e−α(λ)r, where α is capacity the loss coefficient of the medium and is a function of frequency (wavelength). When the medium of propagation is air, the exponential decay term is usually dropped (it only becomes significant at very long distances and high frequencies). Some frequency bands (e.g. 60 GHz) are more sensitive than others to O2 and/or H2O absorption and thus can have a significant decay exponent (as large as 15 dB/Km). So α may not be a strictly increasing function with frequency even though that's what the general trend looks like. The presence of other signal paths of significant strength relative to the LOS path in addition to the LOS (either through reflection or diffraction) can lead to fading and thus renders Equation 1 invalid. Unfortunately, this assumption does not hold in terrestrial networks. First, in a large number of these networks, especially in urban areas, LOS hardly exists, and even when it does, it is hardly the only signal path. These links suffer from fading and shadowing as shown in FIG. 1. (Fading refers to the process in which the different signal paths (rays) arriving at the receiver add-up destructively. Shadowing refers to the process in which the different paths are obstructed either by walls, buildings, or trees.) which dramatically reduces the link budget. However, the most destructive phenomena is the ground reflection shown in FIGS. 2 and 3. Ground reflection, which is difficult to avoid, is harmful for several reasons. Because the ground surface is very large, the reflected path is almost of equal amplitude to the LOS path with a 180° phase shift cause by the reflection. Furthermore, as distance gets larger (with the heights fixed), the length of the reflected path starts approaching that of the primary path. Almost every ray that is reflected off of a building (or an object other than ground) will have a ground reflected ray associated with it, and thus, the attenuation of the aggregate will also follow a ˜1/r4 model. The impact of ground reflection is to transform the effective channel loss from ˜1/r2 to 1/r4 with almost no frequency or spatial diversity.
The poor channel propagation characteristics in terrestrial networks, both urban and suburban, increases the cost and power requirements of the transceivers in order to make for the loss. This increase is not insignificant.
Frequency Dispersion
In addition to fading, one of the consequences of multipath is that different rays may arrive across multiple symbols, especially in high bandwidth systems, giving rise to what is known as intersymbol interference or ISI. ISI gives rise to a multitap channel response in the time-domain and a non-flat response in the frequency domain, which needs to be equalized. Equalization takes place either in the time domain (by applying some form of adaptive filter) or in the frequency domain (e.g. using OFDM). Either way, as a result of equalization, the system will take a hit in the power requirements, processing requirements, and performance. The processing requirements will scale by a factor that is proportional to the ratio of the effective length of the channel (e.g. in seconds) to the symbol (sample) width (in the same time units). The effective length of the channel or the delay spread is the time difference between the first arriving ray (path) with significant power and the last arriving ray with significant power, where the definition of significant is application specific. The symbol width or period is the inverse of the bandwidth. This is significant for both STAP and mm-Wave systems since the computation requirements are already large to begin with. On the power side, equalization algorithms usually result in signals with high peak-to-average power ratios (PAPR). When OFDM is used, the average PAPR increases with the number of subcarriers, which in turn increases with the length of the channel response. When equalization is used, the average PAPR increases with number of filter taps when the filter is applied to the transmitted signal (the filter length is proportional to the channel length). When the filter is only applied at the receive side, the impact will be in the form of noise amplification. The increase in PAPR further reduces the link budget, and thus increases the power requirements on the transceiver. With regard to performance, the amount training required to equalize the channel is proportional to the filter length, and thus adding additional overhead and potentially increasing latency. (The required number of training samples of time-bandwidth product (TBP) is proportional to the product of the number spatial degrees of freedom (DOFs) or the number of antennas and the number of temporal DOFs (i.e. number of filter taps.)
Time Dispersion
Another challenge for terrestrial networks is time varying channels. It's easy to see why the response of wireless channels would vary rapidly when one end of the link is mobile. However, even when both links are fixed, channels can also experience fast fading due movement of reflectors, especially in environments without a strong LOS component. The situation is much worse in high multipath environments since each path length can vary independently at a different rate. In order to cope with fast time variations, the wireless channel needs to be estimated at a much higher frequency. The consequence of this is increased processing requirements, higher overhead, and lower available TBP.
Channel Reciprocity
Implementing STAP at both ends in both directions of wireless link (i.e. both transmitters and receivers are beamforming) is essential to achieving near optimum performance (both the link budget as well as the interference mitigation load on the receiver arrays improve considerably when the transmitters are beamforming and minimizing their interference). Tx beamforming usually represents a bigger challenge since the data required to compute the weights reside on the other end of the link. However, when channel reciprocity holds, this data becomes mirrored locally (i.e. the same data used for Rx beamforming can also be used on the Tx side). Channel reciprocity is more reliable when both directions of the link share the same frequency channel (e.g. TDD). The different transceivers on the array need to be calibrated in order to take advantage of the channel reciprocity. Also, even in a TDD system, there can be a slight mismatch in the channel responses if the channel varies rapidly in time. In the absence of channel reciprocity, the Tx weights need to be learned via explicit feedback from the other end of the link, which may result in considerable overhead or may not be feasible if the channel is changing rapidly in time, or by settling a suboptimal solution (i.e. by estimating the direction(s) of arrival from the Rx weights and the array geometry). In large multipath environments, channel responses on different frequency channels become less correlated, and thus making Tx beamforming work in terrestrial networks only feasible in TDD systems.
Spatial Separation
To take advantage of STAP in order to reuse the spectrum spatially, the remote nodes must have unique spatial signatures that can be separated by the antenna array. In a free space (LOS) environment, the angular separation between every pair of nodes must be larger than the angular resolution of the array. In general, the ability of the array to separate signals with different spatial signatures depends on the size of the array, including the number of antennas. However, in terrestrial network deployments, the nodes are arranged in a single dimension: horizontally (see FIG. 4). This means that the horizontal dimension of the array contributes a lot more to the effective size of the array than the vertical dimension, which makes the array a lot less compact and more difficult to install and maintain.
Installation Challenges
Most wireless systems use one of two types of static-pattern antennas: directional or omni-directional. Omni-directional antennas radiate equally in all directions on a plane. In practice, antennas are classified as omni-directional when they have a wide beam pattern that covers almost 360°, even if the gain is not exactly equal in all directions on the plane of interest. Usually omni-directional antennas are trivial to install and maintain do not require alignment, except maybe for adjusting the plane of orientation. The drawback of using omni-directional antennas is poor range and capacity. Directional antennas require careful beam alignment in both dimensions (azimuth and elevation), and pretty poor in tracking dynamic channels. Beamforming provides significant improvements over static antennas (both directional and omni-directional), and achieves much better range, coverage, and capacity, and adjusts well to channel dynamics, and thus is much easier to install. However, even beamforming antennas can be classified, based on the coverage (steering range) they provide, as directional or omni-directional. Omni-directional beamforming arrays can be implemented by either using omni-directional antenna elements or directional antenna elements that are arranged such that their aggregate pattern provides omni-directional coverage. Omni-directional beamforming antennas sacrifice some system gain and capacity in order to improve coverage and ease of installation. Directional beamforming arrays have limited steerability range. The steerability is usually within a sector that is determined by the beam pattern of the antenna element. The limited steerability of the array will require additional installation effort in order to achieve near optimum performance.
Interference
One of the biggest challenges wireless systems have to deal with is interference, especially when it comes from sources that are external to the system. External interference presents a challenge to wireless system designers because it is difficult to control. Both the timing and power level of the interference signal are difficult to predict, which makes it difficult for the beamformer to cancel it out. In ideal scenarios, a beamformer expects all interfering signals to show up during the reference symbols in order to compute the directions of the nulls. If they only show up in the payload, then the beamformer is unable to cancel it out even if multipass beamforming with decision-direction is employed, especially if there are too many bit errors. Furthermore, the position and power levels of the interference cannot be controlled either. Unlike, in-network interference, where the relative positions of the nodes are chosen to maintain minimum distance and the Tx powers are controlled, nodes that are external to the network can be anywhere.
As shown in FIG. 4, the conventional terrestrial outdoor wireless networks, effective spatial separation only occurs in the azimuth plane, with very limited separation in elevation.
Site Acquisition
Every wireless cell needs to be hosted on a site. These sites vary depending on the required cell size (i.e. coverage area). Macro-cells are usually hosted on towers or tall buildings or towers mounted on building tops etc. Mini-cells are hosted on smaller towers and smaller building and so on. Small cells can either be indoors or outdoors. Outdoor small cells are mounted on either lightposts, street-lights, utility/electricity poles, rooftops, or building walls etc. Each of these sites requires real estate, the size and cost or which grows with the cell size. It's difficult to building a dense network of macro and mini-cells in urban areas because of cost and scarcity of real estate. In addition, cities and municipalities are increasingly passing laws that would limit the deployment of macro and mini-cells for aesthetic and environmental reasons. As a result, most wireless carriers are shifting their attention to small cells as a long term strategy for scaling their capacity. However, small cells present another set of challenges. While the small cell sites are readily available, the acquisition of those sites can still be a hassle. First, access to lightposts, street-lights, or utility poles requires dealing/interacting with several entities, both public and private. This usually includes both the municipality and the utility company. Second, the ownership and rules governing those sites varies from city to city and municipality to municipality. Also, many cities have strict rules on the size and power requirements for equipment mounted on these sites in order to preserve aesthetics. Third, while building walls and rooftops may not have the size/power restrictions associated lightposts, these sites are usually owned/run by different entities, even within the same municipality, with each entity having its own policies. Large network operators don't like dealing with many entities. (That also explains why big carriers usually choose a small number of suppliers for their network equipment, and let these suppliers aggregate and integrate solutions from other entities before they buy it from them.) Fourth and finally, there is the challenge of powering and backhauling those sites. Backhaul is bigger issue for small cells than it is for macro and mini-cells since it becomes a larger fraction of the overall site cost, and it is usually much harder to get LOS and pull fiber to those sites.
Network Bring-Up Time
In addition to cost, building terrestrial networks is a time consuming process. In addition to building the actual infrastructure (e.g. towers, poles . . . ), there is also the process of site acquisition, spectrum acquisition, and dealing with rules and regulations. This whole process makes the barrier to entry much higher and reduces the potential for competition. More importantly, it makes the response very slow in disaster recovery situations, especially when the existing infrastructure is destroyed (e.g. by an earth quake or hurricane).
Coverage
The goal of a wireless system, first and foremost, is to provide ubiquitous coverage to its users. In initial network deployments or in low population density areas, capacity is not the primary concern. In these circumstances, the goal is to achieve coverage with minimal infrastructure while meeting a minimum capacity target. For a terrestrial based infrastructure, achieving universal coverage is not always economically viable. First, the cell tower coverage area is proportional to the square root of its height, which drives up the cost (both capital and maintenance) of the tower. The curvature of the earth surface determines the upper limit on the coverage area. However, since the path loss increases as r4, the effective coverage area is usually much smaller than the upper bound determined by earth curvature. That means that there is minimum number of towers required to achieve the desired coverage. This increases both the capital and operational cost of the network. Second, a large amount of the covered areas will have little or no usage, especially when high density areas are spread out.
The issues listed above can be mitigated when one or both links is up in the air. A link where one or both ends of the link is up in the air is referred to as an aerial link. An example where both ends of the link are in the air is satellite to satellite (or balloon to balloon or balloon to satellite) communication, and an example of the latter would be satellite (balloon) to ground communication. The definition is independent of the position of the satellite orbit (e.g. geo-stationary or leo-stationary). In these types of links, communication is mostly LOS or nLOS. Even when there is multipath, most paths are expected to be clustered around a very narrow angle. This has several major implications on propagation, time/frequency dispersion, reciprocity, spatial separation, installation/maintenance, and interference.
With regard to path-loss, aerial links become mostly LOS or arrive with very few reflections. Also, when a links are vertical to the ground, the length of the ground reflected path becomes independent of the direct path, and in many cases the angular separation between the two paths (i.e. direct and ground reflected) is large enough (usually close to 180°) such that at least one of these paths can be severely attenuated by the antenna pattern, and thus, the signal power for such links drops as 1/r2, as opposed to 1/r4 as in the case for terrestrial links (as shown in FIGS. 2 and 3).
When the channel is mostly LOS (or when most paths come from the same direction at similar delays), the channel response becomes mostly flat in the frequency domain. That means that time/frequency do-main equalization becomes either trivial or unnecessary. This results in significant reduction is processing requirements and TBP. Also, if the channel response is flat enough such that a short time domain filter is sufficient to equalize it, then the benefit from a multicarrier modulation like OFDM starts to diminish. When the channel response in the time domain is short, then a more optimal solution like Viterbi can be used. A single carrier signal provides much better latency, spectral and power efficiency (lower PAPR).
In a general STAP system, the maximum number parameters (DOFs) that need to be estimated equals the product of the number of spatial DOFs (i.e. antennas) and the temporal DOFs. The number of temporal DOFs required to effectively equalize the channel is proportional to maximum delay spread of the channel measured in samples. These parameters can change rapidly in a mobile environment. However, when one or both ends of the link are high up in the air, only a single parameter, the DOA, is required to capture the necessary channel characteristics, and this parameter is not expected to change rapidly, and if it does it will be in a controlled and predictable manner. Even when the nodes are mobile, given the distance between the two nodes, it takes significant time to produce a noticeable difference in the angle of arrival, as shown in FIG. 5.
Another side-effect of having a strong LOS path, the STAP algorithm boils down to DOA computation. The DOA is independent of frequency. So if the DOAs are known, the beamforming weights can be computed for any frequency off-line. The Rx weights can be mapped into Tx weights provided that the all transmitters and receivers are calibrated. Otherwise, it is not possible to map even in a TDD system. Also, for the mapping to work, the separation between any two frequencies has to be small relative to the center frequency in order to guarantee somewhat similar beam patterns. In any case, when the transceivers are accurately calibrated, both beam peaks and nulls can be accurately mapped from one frequency to another. In this case, channel reciprocity is not required, and the links can run in FDD mode in order to reduce the link latency.
When a radio node (master) is up in the air, and all the nodes (slaves) it communicates with are on the ground, and if the maximum horizontal distance from the master to each slave (i.e. cell radius) is comparable to the vertical distance (as shown in FIG. 6, then the master can pack (i.e. spatially multiplex) a lot more slaves with a smaller array than a terrestrial master. Compared to the terrestrial master, in the aerial master, both dimensions of the array are at work in the multiplexing and interference cancellation process as shown in FIG. 6. That means that more spatial DOFs (antennas) can be packed in a smaller area. Furthermore, the antenna alignment becomes trivial at both the master and slaves; antennas are pointed down (up) on the master (slave). Similarly, when both master and slaves are aerial, the elevation of slave relative the master can vary significantly to provide some elevation diversity. However, in the case of aerial links, all arrays must have a 360° field of view, since the nodes are expected to rotate while floating in the air.
Since the cost of increasing the height of an aerial node is marginal and negligible, universal coverage can be achieved with a lot fewer nodes than in the terrestrial case. This makes the economics of rural coverage a lot more attractive. After an initial deployment that achieves full coverage, the heights and densities of these aerial nodes can be changed in order to meet the changing capacity requirements. The cost and complexity of cell-splitting in an aerial network is a lot less than its terrestrial counterpart. More importantly, this eliminates most of the site acquisition costs, and significantly reduces the network bring up time, which is critical in disaster zones. Furthermore, the aerial infrastructure is inherently immune to many types of natural disasters like earthquakes and volcanos as well as ones that are man-made.
Finally, when a radio node is either floating in the air or is on the ground (pointing upwards), it becomes a lot less sensitive to terrestrial in-band or adjacent-band interference. That means that the linearity and out-of-band filtering requirements can be reduced significantly. The potential impact on the cost and power requirements of the system can be reduced further as a result. In the case of full aerial links (both ends are aerial), unlicensed bands can be used with little or no interference to terrestrial networks. This eliminates a large chunk of the spectrum costs, potentially increasing competition by lowering the barrier of entry.
Letting radios float in the air comes with several challenges. The biggest is the constant change in position rotation that often takes place due to wind and other factors. Also, once the nodes are in the air, maintenance becomes difficult. Therefore, adaptive antenna array technology is essential to making aerial networks work reliably without suffering significant outages. Otherwise, the network will experience significant performance degradation. (In this disclosure, the term aerial network is used to refer to networks made of links, where at least one end of the link is aerial.) Using static omni-directional antennas degrades the capacity by creating too much interference, while static directional antennas will result in frequent outages due to the poor coverage.
Modern day aerial networks are mostly built with satellites. Conventional satellite networks achieve more determinism in the satellite position by having satellites in very high orbits in the sky. While this takes care of challenges associated with motion and rotation, and achieves very good coverage with few satellites, it has very poor capacity and latency. The cost of satellite technology is still very high.